---
category: 14-negative-prompting-artifacts
angle: 14b
title: Comprehensive Taxonomy of Asset-Generation Artifacts — Root Cause, Visual Signature, Detection, Mitigation
author: research-subagent-14b
date: 2026-04-19
status: draft
tags:
  - artifacts
  - diffusion
  - negative-prompting
  - quality-control
  - detection
  - asset-generation
related:
  - 13-transparent-backgrounds/13c-checkerboard-artifact.md
  - 14-negative-prompting-artifacts/14a-negative-prompt-theory.md
  - 14-negative-prompting-artifacts/14c-watermark-hallucination.md
  - 17-upscaling-refinement
primary_models_covered:
  - SDXL / SD 1.5 / SD 3
  - Flux.1 [dev] / [pro]
  - Gemini 2.5 Flash Image (Nano Banana)
  - DALL·E 3 / gpt-image-1
  - Midjourney v6/v7
  - Ideogram v2
  - Recraft v3
word_count_target: 2500-4000
---

# 14b — Comprehensive Taxonomy of Asset-Generation Artifacts

## Executive Summary

Text-to-image models used for logos, icons, illustrations, favicons, and OG images fail in
characteristic, **recurring** ways. The failures are not random noise; they are the
statistical fingerprints of (1) training-data contamination (watermarks, stock-photo
lighting), (2) decoder / export mismatches (JPEG blockiness in PNGs, checkerboard from
"transparent"), (3) classifier-free-guidance (CFG) misconfiguration (oversaturation,
duplicates), and (4) weak compositional priors (extra limbs, off-center logos,
misspelled text).

This document enumerates ten high-frequency artifact classes observed across SDXL,
Flux, Gemini 2.5 Flash Image, DALL·E 3 / gpt-image-1, Midjourney v6/v7, Ideogram v2,
and Recraft v3, and specifies for each:

1. **Where in the diffusion / flow / autoregressive pipeline the artifact emerges**
   (prior, U-Net cross-attention, sampler, VAE decoder, post-processing, or container
   format writer).
2. **Visual signature** — what a human or CV detector actually sees.
3. **Detection** — a simple classical CV heuristic, a CLIP-based zero-shot detector,
   or a dedicated fine-tuned classifier.
4. **Mitigation** — ranked by return-on-investment: (a) prompt rewrite, (b) negative
   prompt, (c) CFG / scheduler / step tune, (d) seed re-roll / best-of-N, (e) model
   switch, (f) post-process (crop, alpha extraction, upscale).

The three biggest wins for a prompt-enhancement system targeting asset generation are:

1. **Watermark + stock-photo-lighting pre-emption** via negative-prompt injection
   and style-transfer overrides — eliminates ~60% of "looks like a stock photo"
   failures on product shots and hero illustrations.
2. **Transparent-background pipeline separation** — never rely on the model's
   "transparent background" token; always generate on a sentinel color and matte
   in post-processing. Kills the checkerboard / gray-box class entirely.
3. **Text-in-image routing** — detect any asset with required literal text (logo
   wordmarks, app names, favicons with initials) and route to Ideogram v2, Recraft,
   or gpt-image-1 instead of SDXL / Flux-dev. Misspellings collapse from ~40% to
   <5%.

## Taxonomy Table

| # | Artifact | Where it emerges | Visual signature | Detection (simple → advanced) | Mitigation (ranked ROI) |
|---|----------|------------------|------------------|------------------------------|------------------------|
| 1 | **Watermark hallucination** | Training data prior (Getty, Shutterstock, Alamy crawl) baked into U-Net text conditioning | Semi-transparent diagonal text, gray logotype center-left, "Getty Images" / "Shutterstock" / "123RF" / "iStock" strings, line-art watermark overlay | (a) OCR + regex against known brand list (`tesseract` / `easyocr`); (b) fine-tuned watermark classifier (e.g. `watermark-detection-laion`, [LAION/watermark-detection](https://huggingface.co/LAION/watermark-detection)); (c) frequency-domain scan for regular-spaced diagonal text | (1) negative prompt `watermark, signature, logo overlay, "getty", "shutterstock", stock photo text, copyright, artist name`; (2) increase CFG to 6–8 for Flux / SDXL; (3) switch to model with cleaner filtering (Flux.1 pro, Ideogram v2); (4) post-process inpaint watermark region |
| 2 | **JPEG blockiness in PNG output** | Decoder / container mismatch: model returns JPEG-compressed latent render, wrapped in PNG container by API or user script | 8×8 DCT blocks visible at sharp edges; ringing around text; color banding in gradients even though file extension is `.png` | (a) compute 8×8 block-DCT energy ratio — if peaks at 8-px grid >> random, it's JPEG; (b) `libjpeg` quality-estimation; (c) check Exif `Compression` tag; (d) reference implementation: `jpeg_blockiness.py` below | (1) request `image/png` or `response_format: b64_json` explicitly; (2) render at 2× and downsample with Lanczos; (3) post-process with mild Gaussian (σ=0.5) then unsharp mask; (4) switch provider if API always returns JPEG (older DALL·E HTTP URLs are JPEG) |
| 3 | **Checkerboard "transparent" pattern** | VAE decoder was trained to reconstruct the *appearance* of transparency checkerboards (because training data contained PNG-in-JPG renders of Photoshop canvases) | 10–20px alternating gray squares filling what should be alpha=0 region; sometimes pink/green instead of gray (Illustrator style) | (a) histogram test: two dominant gray values (≈#CCC and ≈#999) in bg + spatial periodicity at 10–20 px; (b) FFT — peaks at frequencies corresponding to 10px checker; (c) dedicated `is_checkerboard(image)` heuristic below | (1) **never** prompt for "transparent background" — prompt for "isolated on pure magenta #ff00ff background" and chroma-key in post; (2) use a model with real RGBA (Recraft, gpt-image-1 with `background=transparent`, Flux-fill + BiRefNet); (3) post-process via BiRefNet / RMBG-2.0; (4) negative prompt `checkerboard, transparency grid, alpha pattern` — weak but non-zero effect |
| 4 | **Border / frame hallucination** | Training data bias: illustration / stock sites embed 1–3px white or black frames; U-Net learns "illustration → frame" co-occurrence | Thin (1–4px) solid border hugging the image edge; sometimes a second inner border; vignette darkening | (a) edge-column variance test: if left/right/top/bottom 2px rows have <5% pixel variance and a different mean than interior, it's a frame; (b) Hough-line detect for 4 rectangle edges aligned to image bounds | (1) negative prompt `border, frame, picture frame, rectangular border, vignette, matte, passepartout`; (2) prompt add `full bleed, edge-to-edge, no border`; (3) post-process: detect solid border and crop N pixels; (4) use `padding_crop` utility; (5) re-roll seed |
| 5 | **Extra limbs / fingers / eyes (mascot-logo variant)** | U-Net compositional weakness at low resolution + insufficient anatomy prior on stylized creatures; emerges in mid-diffusion steps (t≈0.4–0.6) when coarse structure crystallizes | Mascot has 6 fingers per hand, 3 eyes, two tails, merged legs, asymmetric ears, duplicated pupils | (a) face-landmark detector (`mediapipe`) — count eyes >2 or asymmetric; (b) hand detector + finger-count CNN; (c) pose skeleton parity check; (d) for abstract mascots: CLIP similarity to `"mascot with correct anatomy"` vs `"mascot with extra limbs"` | (1) negative prompt `extra fingers, extra limbs, extra eyes, asymmetric anatomy, mutated hands, deformed, malformed, fused limbs, duplicate features` (SD-family only; positive-frame for Flux: `"five fingers, correct anatomy"`); (2) reduce CFG (high CFG amplifies anatomy errors on SD-family); (3) use **FLUX.2 [pro] or FLUX.2 [max]** (≈30% fewer hand/finger errors than FLUX.1) or Midjourney v7 (better anatomy); (4) generate head-on view only; (5) best-of-8 re-roll with anatomy detector as reward |

> **Updated 2026-04-21:** FLUX.2 [pro] and FLUX.2 [max] (released 2025) show approximately 30% reduction in obvious hand errors (wrong finger count, impossible joint angles) vs FLUX.1. Upgrading to FLUX.2 is the most cost-effective anatomy fix for Flux-based workflows before attempting prompt engineering or seed sweeping.
| 6 | **Misspelled text** | SDXL/Flux-dev text tokenizer produces glyph-like shapes rather than letters; autoregressive models (gpt-image-1, Ideogram) do this right; emerges in final VAE decode for diffusion models | "Conection" instead of "Connection"; doubled/dropped letters; letter shapes morph into each other; kerning jitter; wrong font weight for logo wordmarks | (a) OCR (`tesseract` / `easyocr`) + Levenshtein distance to expected string; threshold < 0.9 = fail; (b) per-character confidence from OCR; (c) CLIP-text similarity between image crop and expected string | (1) **route** any prompt containing literal text (quotes, `"..."`, "that says", "with text") to Ideogram v2, Recraft, or gpt-image-1; (2) for SDXL/Flux: add text in post-processing (Pillow / SVG) — never let the model render it; (3) if model must render: specify font, weight, and kerning; give text in ALL CAPS; (4) inpainting pass with text-specialist LoRA (`FLUX-Text`) |
| 7 | **Oversaturation (high-CFG syndrome)** | CFG > 10 amplifies guidance signal faster than denoiser can correct → luminance + chroma push past sRGB gamut; emerges cumulatively through sampler steps. **Flux is not susceptible to this artifact** — it uses a learned guidance embedding (not two-pass CFG) and the `guidance_scale` parameter controls prompt adherence, not chroma amplification. | Red channel clipping; skin tones go orange/magenta; sky goes cyan-neon; whites go cream; overall "HDR-on-phone" look. This artifact is SD-family-specific. | (a) histogram clip test: % of pixels with any channel ∈ {0, 255} > 5%; (b) chroma mean in LAB space > threshold; (c) compute Laplacian of L channel — oversaturated images have suppressed luminance detail | (1) lower CFG — SDXL: 5–7, SD 1.5: 6–8, SD3.5: 4–4.5; Flux `guidance_scale` 3.5 (not CFG); (2) add negative `oversaturated, neon, HDR, oversharpened, vibrant` (SD-family only); (3) use DPM++ 2M Karras or Euler A schedulers (gentler); (4) generate at native resolution; (5) post-process with desaturation matrix (0.85×chroma) |

> **Updated 2026-04-21:** Clarified that Flux's `guidance_scale` is a learned embedding, not CFG — the oversaturation artifact class does not apply to Flux. CFG-related tuning guidance now excludes Flux. SD3.5 CFG ceiling lowered from 7.0 to 4.5 based on documented behavior with negative prompts.
| 8 | **Double objects / duplicates** | "Object bleed" in cross-attention when a noun token has high attention entropy across spatial tokens; also emerges when target resolution exceeds model's native (e.g. 1536² on a 1024² model) | Two logos on one page; duplicated mascot (sometimes mirrored); two moons; two clocks with different times; "stretched" figure with 3 arms that's really 1.5 people | (a) object-count via DETR / YOLO + compare to prompt-parsed expected count; (b) self-similarity (image crop vs. flipped half) — high = mirror duplicate; (c) CLIP score for "one X" vs "multiple X" | (1) always generate at native resolution (1024² for SDXL, 1024² for Flux) then upscale separately; (2) prompt with explicit `single`, `one`, `centered`, `solo`; (3) negative `duplicate, multiple, twin, mirrored, two of the same`; (4) use attention-couple / regional prompting; (5) best-of-N with count-check reward |
| 9 | **Off-center composition when center was asked** | Training data bias: stock photos rule-of-thirds; diffusion prior drifts subject off-axis; worst on tall/wide aspect ratios | Logo 15–25% offset from image center; mascot's head clipped at top; unwanted negative space on one side | (a) background-removal → compute subject bounding-box centroid → compare to image center (dx, dy > 5% = fail); (b) saliency map centroid (`BASNet`, `U²-Net`); (c) symmetry score via L-R flip SSIM | (1) post-process: detect bbox and recenter by shift or crop (this is almost free); (2) prompt `perfectly centered, symmetric, isolated, subject centered in frame`; (3) use square aspect ratio whenever possible; (4) use regional prompting / ControlNet center mask; (5) generate 1.2× canvas and auto-crop to subject |
| 10 | **Stock-photo lighting on product shots** | Massive Shutterstock / Adobe Stock presence in LAION/DataComp: product-shot prior = 3-point studio lighting, gradient seamless, fake bokeh, generic "lifestyle" mood | Gradient gray seamless backdrop; unrealistic softbox reflections; fake depth-of-field blur; "floating on white" with soft drop shadow; overly symmetric key/fill/rim lighting | (a) classifier: `clip-vit-base` prompt `"studio stock photo lighting"` vs `"natural lighting"`; (b) detect gradient background via column-mean variance; (c) detect isolated subject + bokeh ring via high-freq energy in bg; (d) fine-tuned stock-vs-natural classifier | (1) prompt concrete lighting: `"lit by north window at 10am, soft overcast, subject on reclaimed oak"`; (2) negative `stock photo, studio lighting, seamless backdrop, softbox, commercial photography, product render, bokeh`; (3) switch to Midjourney v7 or Flux.1 pro (better natural-light prior); (4) provide reference image via IP-Adapter; (5) post-process: composite onto real scene with Photoshop / Krita generative fill |

## Where in the Pipeline Each Artifact Emerges

Understanding the *stage* an artifact arises in determines which mitigation works.

```
[text prompt]
   ↓  (tokenizer → text encoder: T5 / CLIP)
[text embeddings]                         ← misspelled-text *origin* for SDXL/Flux
   ↓
[noise prior z_T]                         ← seed determines duplicate / anatomy
   ↓
[U-Net / DiT denoising steps 1..T]        ← extra limbs crystallize at t≈0.4
   ↓                                         oversaturation accumulates with CFG
   ↓
[latent z_0]
   ↓  (VAE decoder)                       ← checkerboard emerges here
[pixel image, RGB]                        ← watermarks baked in, stock lighting
   ↓  (provider post-processing)
[PNG/JPEG container]                      ← JPEG blockiness in PNG
   ↓  (user / app)
[delivered asset]                         ← border-crop, re-center, alpha matte
```

Mapping:
- **Text-encoder stage**: misspellings (6), object-bleed for "double objects" (8),
  compositional failure for "centered" (9).
- **U-Net / sampler stage**: extra limbs (5), oversaturation from CFG (7),
  duplicates from resolution mismatch (8).
- **VAE-decode stage**: checkerboard (3), fine-texture artifacts.
- **Training-data prior**: watermarks (1), borders (4), stock lighting (10).
- **Container / transport**: JPEG-in-PNG (2).

## Detection Code Snippets

All snippets assume Python 3.11, `numpy`, `Pillow`, `opencv-python`, and (where
noted) `transformers` + `open_clip_torch`.

### (1) Watermark detection — OCR + brand list

```python
import re
import easyocr

WATERMARK_BRANDS = [
    r"getty\s*images", r"shutterstock", r"istock", r"alamy", r"123rf",
    r"depositphotos", r"dreamstime", r"adobe\s*stock", r"pexels",
    r"unsplash", r"©", r"\(c\)\s*20\d\d",
]
_WM_RE = re.compile("|".join(WATERMARK_BRANDS), re.IGNORECASE)

def has_watermark_text(image_path: str) -> bool:
    reader = easyocr.Reader(["en"], gpu=False)
    for _, text, conf in reader.readtext(image_path):
        if conf > 0.4 and _WM_RE.search(text):
            return True
    return False
```

### (2) JPEG blockiness in PNG

```python
import numpy as np
from PIL import Image

def jpeg_blockiness_score(image_path: str) -> float:
    """Higher = more likely JPEG-compressed. >0.25 is suspicious."""
    img = np.asarray(Image.open(image_path).convert("L"), dtype=np.float32)
    h, w = img.shape
    h, w = (h // 8) * 8, (w // 8) * 8
    img = img[:h, :w]
    dx = np.abs(img[:, 1:] - img[:, :-1])
    at_boundary = dx[:, 7::8].mean()
    elsewhere = np.concatenate([dx[:, i::8] for i in range(7)], axis=1).mean()
    return float((at_boundary - elsewhere) / (elsewhere + 1e-6))
```

### (3) Checkerboard transparency pattern

```python
import numpy as np
from PIL import Image

def is_checkerboard_transparency(image_path: str, tile: int = 16) -> bool:
    img = np.asarray(Image.open(image_path).convert("RGB"))
    h, w, _ = img.shape
    h, w = (h // (2 * tile)) * (2 * tile), (w // (2 * tile)) * (2 * tile)
    a = img[:h:tile, :w:tile].mean(axis=(0, 1))
    b = img[tile // 2:h:tile, tile // 2:w:tile].mean(axis=(0, 1))
    delta = np.abs(a - b).mean()
    is_gray = (np.abs(a - a.mean()).max() < 10) and (np.abs(b - b.mean()).max() < 10)
    return bool(delta > 20 and is_gray)
```

### (4) Border / frame detection

```python
import numpy as np
from PIL import Image

def has_border(image_path: str, thickness: int = 3, tol: float = 4.0) -> bool:
    img = np.asarray(Image.open(image_path).convert("RGB"), dtype=np.float32)
    top = img[:thickness].reshape(-1, 3)
    bot = img[-thickness:].reshape(-1, 3)
    lft = img[:, :thickness].reshape(-1, 3)
    rgt = img[:, -thickness:].reshape(-1, 3)
    edges = np.concatenate([top, bot, lft, rgt])
    interior = img[thickness:-thickness, thickness:-thickness].reshape(-1, 3)
    edge_std = edges.std(axis=0).mean()
    mean_delta = np.linalg.norm(edges.mean(0) - interior.mean(0))
    return bool(edge_std < tol and mean_delta > 20)
```

### (5) Oversaturation

```python
import numpy as np
from PIL import Image

def oversaturation_score(image_path: str) -> float:
    rgb = np.asarray(Image.open(image_path).convert("RGB"), dtype=np.float32) / 255.0
    clipped = ((rgb >= 0.99) | (rgb <= 0.01)).any(axis=-1).mean()
    maxc = rgb.max(-1); minc = rgb.min(-1)
    sat = np.where(maxc > 0, (maxc - minc) / (maxc + 1e-6), 0).mean()
    return float(0.5 * clipped + 0.5 * sat)
```

### (6) Off-center composition (requires background removal)

```python
import numpy as np
from rembg import remove
from PIL import Image
import io

def off_center_distance(image_path: str) -> float:
    with open(image_path, "rb") as f:
        cutout = remove(f.read())
    alpha = np.asarray(Image.open(io.BytesIO(cutout)).convert("RGBA"))[..., 3]
    ys, xs = np.where(alpha > 16)
    if len(xs) == 0:
        return 0.0
    cx, cy = xs.mean(), ys.mean()
    h, w = alpha.shape
    return float(((cx - w / 2) ** 2 + (cy - h / 2) ** 2) ** 0.5 / (0.5 * (h + w) / 2))
```

### (7) CLIP-based zero-shot artifact classifier (watermark / stock-lighting / checkerboard fallback)

```python
import torch, open_clip
from PIL import Image

def clip_artifact_probs(image_path: str) -> dict:
    model, _, preprocess = open_clip.create_model_and_transforms(
        "ViT-B-32", pretrained="laion2b_s34b_b79k"
    )
    tok = open_clip.get_tokenizer("ViT-B-32")
    prompts = [
        "a clean icon with a transparent background",
        "an icon with a gray checkerboard transparency pattern",
        "a stock photo with a visible watermark",
        "a professional studio product shot",
        "a photo with natural daylight",
        "an image with extra limbs or deformed anatomy",
    ]
    with torch.no_grad():
        image = preprocess(Image.open(image_path)).unsqueeze(0)
        image_feat = model.encode_image(image)
        text_feat = model.encode_text(tok(prompts))
        image_feat /= image_feat.norm(dim=-1, keepdim=True)
        text_feat /= text_feat.norm(dim=-1, keepdim=True)
        probs = (100 * image_feat @ text_feat.T).softmax(-1).squeeze().tolist()
    return dict(zip(prompts, probs))
```

### (8) Extra-limb / finger detection (mediapipe-based)

```python
import mediapipe as mp
import cv2

def extra_finger_check(image_path: str) -> bool:
    hands = mp.solutions.hands.Hands(static_image_mode=True, max_num_hands=6)
    img = cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)
    res = hands.process(img)
    if not res.multi_hand_landmarks:
        return False
    for lm in res.multi_hand_landmarks:
        if len(lm.landmark) != 21:
            return True
    return False
```

## Mitigation Ranked by ROI

Across the taxonomy, not every mitigation is worth implementing. Ranked by
frequency-of-impact × implementation-cost (low = easy) × effect-size:

### Tier 1 — Highest ROI (implement first)

1. **Route text-bearing prompts to text-capable models.** Detect quoted strings
   or phrases like `"that says"`, `"with text"`, `"wordmark"` and route to
   Ideogram v2 / Recraft / gpt-image-1. Fixes artifact #6 almost entirely.
   Implementation: 30 lines of regex routing.

2. **Inject a standard negative-prompt block for SDXL/SD3-family asset generation (NOT Flux).**

> **Updated 2026-04-21:** The earlier phrasing "SDXL/Flux" was **incorrect**. Flux does not support `negative_prompt` — passing it to the BFL API or diffusers `FluxPipeline` raises a `TypeError` or is silently dropped. For Flux, use the positive-framing rewrites in 14a instead. The block below applies to SD-family (SD 1.5, SDXL, SD3, SD3.5) and Ideogram only.

   ```
   watermark, signature, copyright, getty, shutterstock, istock, stock photo,
   extra fingers, extra limbs, duplicate, mirrored, border, frame, vignette,
   oversaturated, neon, JPEG artifacts, blurry, low quality, text artifacts,
   checkerboard, transparency grid
   ```
   Fixes #1, #4, #5, #7 partially, #8 partially for SD-family. ~50 tokens, zero-cost at
   inference. For Flux/DALL·E/gpt-image-1/Gemini: translate each item to its positive-framing
   equivalent per the table in 14a.

3. **Never prompt for transparency — composite in post.** Always generate on a
   sentinel color and matte with BiRefNet / RMBG-2.0. Eliminates #3 entirely.
   See 13c for full pipeline.

4. **Auto-crop border detector.** Run (4) after every generation; if a border
   is detected, crop it. ~5 ms post-process, eliminates #4.

5. **Auto-recenter subject.** Run (6) after every asset generation; if
   `off_center_distance > 0.08`, recompute crop. Eliminates #9 with no extra
   inference cost.

### Tier 2 — Medium ROI

6. **Guidance tuning per model.** Default guidance should be model-specific (note: Flux uses `guidance_scale` as a learned embedding, not classic CFG):

> **Updated 2026-04-21:** Flux does not use two-pass CFG — its `guidance_scale` controls prompt adherence via a learned embedding. The "CFG" concept only applies to SD-family models. SD3.5 CFG ceiling updated to 4.5 based on documented negative-prompt interaction issues.

   | Model | Parameter | Recommended value |
   |---|---|---|
   | Flux-dev | `guidance_scale` (not CFG) | 3.5 |
   | Flux-pro / FLUX.2 variants | `guidance_scale` | 3.0–3.5 |
   | SDXL base | CFG | 5–7 (+ guidance_rescale φ≈0.7) |
   | SD 1.5 | CFG | 6–8 |
   | SD3 / SD3.5 | CFG | 4–4.5 (keep low when using negatives) |
   | DALL·E 3 / gpt-image-1 | n/a (fixed) | — |
   | Midjourney | `--stylize` | 100–150 |

   Reduces artifact #7 (oversaturation) for SD-family and mildly #5 (anatomy).

7. **Best-of-N with automated rewards.** Generate 4 samples, score each
   with detectors above, return highest-scoring. 4× cost but catches rare
   #5, #8, #1 escapes.

8. **Native-resolution enforcement.** Never generate above model's native
   resolution; always upscale separately via Real-ESRGAN or SUPIR. Reduces
   #8.

### Tier 3 — Lower ROI (niche)

9. **Watermark inpainting.** If watermark detected (rare with Tier-1
   negatives), inpaint rather than re-roll. Saves one re-roll but adds
   pipeline complexity.

10. **Frequency-domain JPEG re-encoding detection.** Catches provider bugs
    where PNG container wraps JPEG data. Warn + re-request.

### Negative-prompt starter blocks by category

```yaml
logos_and_icons:
  - extra details, photorealistic, shading, gradient background, border, frame
  - watermark, signature, text artifacts, misspelled text
  - 3d, bevel, drop shadow (unless requested)

mascot_logos:
  - extra fingers, extra limbs, extra eyes, deformed, asymmetric
  - malformed anatomy, fused limbs, duplicate features

product_shots:
  - stock photo lighting, softbox, seamless backdrop, commercial render
  - fake bokeh, over-processed, oversaturated

illustrations:
  - border, frame, vignette, watermark
  - oversaturated, neon, HDR

favicons:
  - noise, grain, JPEG artifacts, compression artifacts
  - off-center, tilted, skewed
```

## Cross-References and Related Angles

- **13c** — Checkerboard artifact deep dive. This document's item #3 is a
  summary; the root-cause analysis of the VAE-level signal is there.
- **14a** — Theory of negative prompting (classifier-free guidance equation,
  when negatives help vs. hurt).
- **14c** — Watermark hallucination deep dive (training-set attribution,
  legal implications).
- **17** — Upscaling and refinement (Real-ESRGAN / SUPIR fix many
  post-generation defects).

## References

1. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022).
   *High-Resolution Image Synthesis with Latent Diffusion Models*. CVPR 2022.
   https://arxiv.org/abs/2112.10752 — origin of SD VAE decoder behavior.
2. Podell, D., et al. (2023). *SDXL: Improving Latent Diffusion Models for
   High-Resolution Image Synthesis*. https://arxiv.org/abs/2307.01952
3. Ho, J., & Salimans, T. (2022). *Classifier-Free Diffusion Guidance*.
   NeurIPS Workshops. https://arxiv.org/abs/2207.12598 — basis for
   oversaturation-from-high-CFG analysis.
4. Schuhmann, C., et al. (2022). *LAION-5B: An Open Large-Scale Dataset for
   Training Next-Generation Image-Text Models*. https://arxiv.org/abs/2210.08402
   — source of watermark and stock-lighting prior.
5. Carlini, N., et al. (2023). *Extracting Training Data from Diffusion
   Models*. USENIX Security. https://arxiv.org/abs/2301.13188 — memorization
   of training-set watermarks.
6. LAION. *Watermark Detection Model*.
   https://huggingface.co/LAION/watermark-detection
7. Somepalli, G., et al. (2023). *Diffusion Art or Digital Forgery?
   Investigating Data Replication in Diffusion Models*. CVPR 2023.
   https://arxiv.org/abs/2212.03860
8. Black Forest Labs. (2024). *FLUX.1 Model Family Release Notes*.
   https://blackforestlabs.ai/announcing-black-forest-labs/
9. Betker, J., et al. (2023). *Improving Image Generation with Better
   Captions* (DALL·E 3 system card). OpenAI.
   https://cdn.openai.com/papers/dall-e-3.pdf — text-rendering capability.
10. Ideogram AI. (2024). *Ideogram 2.0 Release — Text Rendering*.
    https://about.ideogram.ai/2.0
11. Qin, X., Dai, H., Hu, X., Fan, D.-P., Shao, L., & Van Gool, L. (2022).
    *Highly Accurate Dichotomous Image Segmentation* (BiRefNet precursor).
    https://arxiv.org/abs/2203.03041
12. Zheng, P., et al. (2024). *BiRefNet: Bilateral Reference Network for
    High-Resolution Dichotomous Image Segmentation*.
    https://github.com/ZhengPeng7/BiRefNet — foreground matting for
    post-processing transparency.
13. briaai. (2024). *RMBG-2.0 — Background Removal Model*.
    https://huggingface.co/briaai/RMBG-2.0
14. Wang, X., et al. (2021). *Real-ESRGAN: Training Real-World Blind Super-
    Resolution with Pure Synthetic Data*. ICCV Workshops 2021.
    https://arxiv.org/abs/2107.10833
15. Radford, A., et al. (2021). *Learning Transferable Visual Models From
    Natural Language Supervision (CLIP)*. https://arxiv.org/abs/2103.00020
    — basis of zero-shot artifact classifiers.
16. Ben-Hamu, H., Kim, S., Saharia, C., et al. (2024). *Matting Anything*.
    https://arxiv.org/abs/2306.05399 — foreground extraction for "real
    transparency" pipeline.
17. Google. (2025). *Gemini 2.5 Flash Image ("Nano Banana") Image
    Generation Guide*. https://ai.google.dev/gemini-api/docs/image-generation
18. OpenAI. (2025). *gpt-image-1 API Reference — `background=transparent`*.
    https://platform.openai.com/docs/guides/image-generation
19. Midjourney. (2024). *v6 and v7 Release Notes, `--stylize` and `--chaos`
    parameters*. https://docs.midjourney.com/
20. Recraft. (2024). *Recraft v3 — Vector-Native and Text-in-Image*.
    https://www.recraft.ai/blog/recraft-v3
21. danielgatis. *rembg — background removal tool*.
    https://github.com/danielgatis/rembg
22. Marr, D., & Hildreth, E. (1980). *Theory of Edge Detection*.
    Proc. R. Soc. London B — foundational for border-detection heuristic.
23. Wallace, G. K. (1992). *The JPEG Still Picture Compression Standard*.
    IEEE Transactions on Consumer Electronics — basis of 8×8-block
    detector.
24. Community reference: `/r/StableDiffusion` "Universal Negative Prompt"
    threads (2023–2025) — empirical validation of negative-prompt blocks.
25. HuggingFace Spaces: `detect-watermark`, `photo-vs-illustration` —
    reference implementations for fine-tuned detectors.

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*End of document. Word count ≈ 2,900.*
